Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations270000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.0 MiB
Average record size in memory128.0 B

Variable types

Numeric10
Categorical5
Boolean1

Alerts

age is highly overall correlated with cred_hist_length and 1 other fieldsHigh correlation
cred_hist_length is highly overall correlated with age and 1 other fieldsHigh correlation
emp_exp is highly overall correlated with age and 1 other fieldsHigh correlation
loan_amount is highly overall correlated with percent_incomeHigh correlation
loan_id is highly overall correlated with person_idHigh correlation
loan_status is highly overall correlated with previous_defaultsHigh correlation
percent_income is highly overall correlated with loan_amountHigh correlation
person_id is highly overall correlated with loan_idHigh correlation
previous_defaults is highly overall correlated with loan_statusHigh correlation
income is highly skewed (γ1 = 34.13663485) Skewed
loan_id is uniformly distributed Uniform
person_id is uniformly distributed Uniform
loan_id has unique values Unique
person_id has unique values Unique
emp_exp has 57396 (21.3%) zeros Zeros

Reproduction

Analysis started2024-12-21 23:30:37.565767
Analysis finished2024-12-21 23:31:04.855742
Duration27.29 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

loan_id
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct270000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135000.5
Minimum1
Maximum270000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-21T18:31:04.973183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13500.95
Q167500.75
median135000.5
Q3202500.25
95-th percentile256500.05
Maximum270000
Range269999
Interquartile range (IQR)134999.5

Descriptive statistics

Standard deviation77942.431
Coefficient of variation (CV)0.5773492
Kurtosis-1.2
Mean135000.5
Median Absolute Deviation (MAD)67500
Skewness0
Sum3.6450135 × 1010
Variance6.0750225 × 109
MonotonicityStrictly increasing
2024-12-21T18:31:05.126069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
179990 1
 
< 0.1%
179992 1
 
< 0.1%
179993 1
 
< 0.1%
179994 1
 
< 0.1%
179995 1
 
< 0.1%
179996 1
 
< 0.1%
179997 1
 
< 0.1%
179998 1
 
< 0.1%
179999 1
 
< 0.1%
Other values (269990) 269990
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
270000 1
< 0.1%
269999 1
< 0.1%
269998 1
< 0.1%
269997 1
< 0.1%
269996 1
< 0.1%
269995 1
< 0.1%
269994 1
< 0.1%
269993 1
< 0.1%
269992 1
< 0.1%
269991 1
< 0.1%

person_id
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct270000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135000.5
Minimum1
Maximum270000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-21T18:31:05.276651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13500.95
Q167500.75
median135000.5
Q3202500.25
95-th percentile256500.05
Maximum270000
Range269999
Interquartile range (IQR)134999.5

Descriptive statistics

Standard deviation77942.431
Coefficient of variation (CV)0.5773492
Kurtosis-1.2
Mean135000.5
Median Absolute Deviation (MAD)67500
Skewness0
Sum3.6450135 × 1010
Variance6.0750225 × 109
MonotonicityStrictly increasing
2024-12-21T18:31:05.430347image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
179990 1
 
< 0.1%
179992 1
 
< 0.1%
179993 1
 
< 0.1%
179994 1
 
< 0.1%
179995 1
 
< 0.1%
179996 1
 
< 0.1%
179997 1
 
< 0.1%
179998 1
 
< 0.1%
179999 1
 
< 0.1%
Other values (269990) 269990
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
270000 1
< 0.1%
269999 1
< 0.1%
269998 1
< 0.1%
269997 1
< 0.1%
269996 1
< 0.1%
269995 1
< 0.1%
269994 1
< 0.1%
269993 1
< 0.1%
269992 1
< 0.1%
269991 1
< 0.1%

age
Real number (ℝ)

High correlation 

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.764178
Minimum20
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-21T18:31:05.872132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q124
median26
Q330
95-th percentile39
Maximum144
Range124
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.0450522
Coefficient of variation (CV)0.21772848
Kurtosis18.647611
Mean27.764178
Median Absolute Deviation (MAD)3
Skewness2.5480832
Sum7496328
Variance36.542657
MonotonicityNot monotonic
2024-12-21T18:31:06.032767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 31524
11.7%
24 30828
11.4%
25 27042
10.0%
22 25416
9.4%
26 21954
 
8.1%
27 18570
 
6.9%
28 16368
 
6.1%
29 14730
 
5.5%
30 12126
 
4.5%
31 9870
 
3.7%
Other values (50) 61572
22.8%
ValueCountFrequency (%)
20 102
 
< 0.1%
21 7734
 
2.9%
22 25416
9.4%
23 31524
11.7%
24 30828
11.4%
25 27042
10.0%
26 21954
8.1%
27 18570
6.9%
28 16368
6.1%
29 14730
5.5%
ValueCountFrequency (%)
144 18
< 0.1%
123 12
< 0.1%
116 6
 
< 0.1%
109 6
 
< 0.1%
94 6
 
< 0.1%
84 6
 
< 0.1%
80 6
 
< 0.1%
78 6
 
< 0.1%
76 6
 
< 0.1%
73 18
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
male
149046 
female
120954 

Length

Max length6
Median length4
Mean length4.8959556
Min length4

Characters and Unicode

Total characters1321908
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 149046
55.2%
female 120954
44.8%

Length

2024-12-21T18:31:06.291700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T18:31:06.405881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
male 149046
55.2%
female 120954
44.8%

Most occurring characters

ValueCountFrequency (%)
e 390954
29.6%
m 270000
20.4%
a 270000
20.4%
l 270000
20.4%
f 120954
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1321908
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 390954
29.6%
m 270000
20.4%
a 270000
20.4%
l 270000
20.4%
f 120954
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1321908
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 390954
29.6%
m 270000
20.4%
a 270000
20.4%
l 270000
20.4%
f 120954
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1321908
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 390954
29.6%
m 270000
20.4%
a 270000
20.4%
l 270000
20.4%
f 120954
 
9.1%

education
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Bachelor
80394 
Associate
72168 
High School
71832 
Master
41880 
Doctorate
 
3726

Length

Max length11
Median length9
Mean length8.769
Min length6

Characters and Unicode

Total characters2367630
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaster
2nd rowHigh School
3rd rowHigh School
4th rowBachelor
5th rowMaster

Common Values

ValueCountFrequency (%)
Bachelor 80394
29.8%
Associate 72168
26.7%
High School 71832
26.6%
Master 41880
15.5%
Doctorate 3726
 
1.4%

Length

2024-12-21T18:31:06.513244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T18:31:06.622419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
bachelor 80394
23.5%
associate 72168
21.1%
high 71832
21.0%
school 71832
21.0%
master 41880
12.3%
doctorate 3726
 
1.1%

Most occurring characters

ValueCountFrequency (%)
o 303678
12.8%
c 228120
9.6%
h 224058
9.5%
e 198168
8.4%
a 198168
8.4%
s 186216
 
7.9%
l 152226
 
6.4%
i 144000
 
6.1%
r 126000
 
5.3%
t 121500
 
5.1%
Other values (8) 485496
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2367630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 303678
12.8%
c 228120
9.6%
h 224058
9.5%
e 198168
8.4%
a 198168
8.4%
s 186216
 
7.9%
l 152226
 
6.4%
i 144000
 
6.1%
r 126000
 
5.3%
t 121500
 
5.1%
Other values (8) 485496
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2367630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 303678
12.8%
c 228120
9.6%
h 224058
9.5%
e 198168
8.4%
a 198168
8.4%
s 186216
 
7.9%
l 152226
 
6.4%
i 144000
 
6.1%
r 126000
 
5.3%
t 121500
 
5.1%
Other values (8) 485496
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2367630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 303678
12.8%
c 228120
9.6%
h 224058
9.5%
e 198168
8.4%
a 198168
8.4%
s 186216
 
7.9%
l 152226
 
6.4%
i 144000
 
6.1%
r 126000
 
5.3%
t 121500
 
5.1%
Other values (8) 485496
20.5%

income
Real number (ℝ)

Skewed 

Distinct33989
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80319.053
Minimum8000
Maximum7200766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-21T18:31:06.757958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8000
5-th percentile28366.7
Q147204
median67048
Q395789.25
95-th percentile166754.7
Maximum7200766
Range7192766
Interquartile range (IQR)48585.25

Descriptive statistics

Standard deviation80421.754
Coefficient of variation (CV)1.0012787
Kurtosis2398.4626
Mean80319.053
Median Absolute Deviation (MAD)23124
Skewness34.136635
Sum2.1686144 × 1010
Variance6.4676585 × 109
MonotonicityNot monotonic
2024-12-21T18:31:06.908744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 90
 
< 0.1%
73011 60
 
< 0.1%
36995 54
 
< 0.1%
60914 48
 
< 0.1%
37020 48
 
< 0.1%
73082 42
 
< 0.1%
60864 42
 
< 0.1%
67131 42
 
< 0.1%
72951 42
 
< 0.1%
73040 42
 
< 0.1%
Other values (33979) 269490
99.8%
ValueCountFrequency (%)
8000 90
< 0.1%
8037 6
 
< 0.1%
8104 6
 
< 0.1%
8186 6
 
< 0.1%
8248 6
 
< 0.1%
8267 6
 
< 0.1%
8277 6
 
< 0.1%
8302 6
 
< 0.1%
8518 6
 
< 0.1%
9364 6
 
< 0.1%
ValueCountFrequency (%)
7200766 6
< 0.1%
5556399 6
< 0.1%
5545545 6
< 0.1%
2448661 6
< 0.1%
2280980 6
< 0.1%
2139143 6
< 0.1%
2012954 6
< 0.1%
1741243 6
< 0.1%
1728974 6
< 0.1%
1661567 6
< 0.1%

emp_exp
Real number (ℝ)

High correlation  Zeros 

Distinct63
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4103333
Minimum0
Maximum125
Zeros57396
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-21T18:31:07.062856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q38
95-th percentile17
Maximum125
Range125
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.0634759
Coefficient of variation (CV)1.1207213
Kurtosis19.166438
Mean5.4103333
Median Absolute Deviation (MAD)3
Skewness2.5948453
Sum1460790
Variance36.765741
MonotonicityNot monotonic
2024-12-21T18:31:07.218977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57396
21.3%
2 24804
9.2%
1 24366
9.0%
3 23340
8.6%
4 21144
 
7.8%
5 18000
 
6.7%
6 16302
 
6.0%
7 13224
 
4.9%
8 11340
 
4.2%
9 9450
 
3.5%
Other values (53) 50634
18.8%
ValueCountFrequency (%)
0 57396
21.3%
1 24366
9.0%
2 24804
9.2%
3 23340
8.6%
4 21144
 
7.8%
5 18000
 
6.7%
6 16302
 
6.0%
7 13224
 
4.9%
8 11340
 
4.2%
9 9450
 
3.5%
ValueCountFrequency (%)
125 6
< 0.1%
124 6
< 0.1%
121 6
< 0.1%
101 6
< 0.1%
100 6
< 0.1%
93 6
< 0.1%
85 6
< 0.1%
76 6
< 0.1%
62 6
< 0.1%
61 6
< 0.1%

home_ownership
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
RENT
140658 
MORTGAGE
110934 
OWN
17706 
OTHER
 
702

Length

Max length8
Median length4
Mean length5.5804889
Min length3

Characters and Unicode

Total characters1506732
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowOWN
3rd rowMORTGAGE
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
RENT 140658
52.1%
MORTGAGE 110934
41.1%
OWN 17706
 
6.6%
OTHER 702
 
0.3%

Length

2024-12-21T18:31:07.360135image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T18:31:07.466468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
rent 140658
52.1%
mortgage 110934
41.1%
own 17706
 
6.6%
other 702
 
0.3%

Most occurring characters

ValueCountFrequency (%)
R 252294
16.7%
E 252294
16.7%
T 252294
16.7%
G 221868
14.7%
N 158364
10.5%
O 129342
8.6%
M 110934
7.4%
A 110934
7.4%
W 17706
 
1.2%
H 702
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1506732
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 252294
16.7%
E 252294
16.7%
T 252294
16.7%
G 221868
14.7%
N 158364
10.5%
O 129342
8.6%
M 110934
7.4%
A 110934
7.4%
W 17706
 
1.2%
H 702
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1506732
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 252294
16.7%
E 252294
16.7%
T 252294
16.7%
G 221868
14.7%
N 158364
10.5%
O 129342
8.6%
M 110934
7.4%
A 110934
7.4%
W 17706
 
1.2%
H 702
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1506732
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 252294
16.7%
E 252294
16.7%
T 252294
16.7%
G 221868
14.7%
N 158364
10.5%
O 129342
8.6%
M 110934
7.4%
A 110934
7.4%
W 17706
 
1.2%
H 702
 
< 0.1%

loan_amount
Real number (ℝ)

High correlation 

Distinct4483
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9583.1576
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-21T18:31:07.589684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2000
Q15000
median8000
Q312237.25
95-th percentile24000
Maximum35000
Range34500
Interquartile range (IQR)7237.25

Descriptive statistics

Standard deviation6314.8282
Coefficient of variation (CV)0.65895068
Kurtosis1.350979
Mean9583.1576
Median Absolute Deviation (MAD)3800
Skewness1.1796985
Sum2.5874525 × 109
Variance39877055
MonotonicityNot monotonic
2024-12-21T18:31:07.737034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 21702
 
8.0%
5000 16722
 
6.2%
6000 14556
 
5.4%
12000 14496
 
5.4%
15000 12024
 
4.5%
8000 11568
 
4.3%
4000 8436
 
3.1%
20000 8310
 
3.1%
3000 8268
 
3.1%
7000 7884
 
2.9%
Other values (4473) 146034
54.1%
ValueCountFrequency (%)
500 30
< 0.1%
563 6
 
< 0.1%
700 6
 
< 0.1%
725 6
 
< 0.1%
750 6
 
< 0.1%
800 6
 
< 0.1%
900 12
 
< 0.1%
912 6
 
< 0.1%
922 6
 
< 0.1%
950 6
 
< 0.1%
ValueCountFrequency (%)
35000 1404
0.5%
34826 6
 
< 0.1%
34800 6
 
< 0.1%
34664 6
 
< 0.1%
34375 6
 
< 0.1%
34322 6
 
< 0.1%
34121 6
 
< 0.1%
34000 24
 
< 0.1%
33950 12
 
< 0.1%
33800 6
 
< 0.1%

loan_intent
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
EDUCATION
54918 
MEDICAL
51288 
VENTURE
46914 
PERSONAL
45312 
DEBTCONSOLIDATION
42870 

Length

Max length17
Median length15
Mean length10.012711
Min length7

Characters and Unicode

Total characters2703432
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPERSONAL
2nd rowEDUCATION
3rd rowMEDICAL
4th rowMEDICAL
5th rowMEDICAL

Common Values

ValueCountFrequency (%)
EDUCATION 54918
20.3%
MEDICAL 51288
19.0%
VENTURE 46914
17.4%
PERSONAL 45312
16.8%
DEBTCONSOLIDATION 42870
15.9%
HOMEIMPROVEMENT 28698
10.6%

Length

2024-12-21T18:31:07.881212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T18:31:07.999837image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
education 54918
20.3%
medical 51288
19.0%
venture 46914
17.4%
personal 45312
16.8%
debtconsolidation 42870
15.9%
homeimprovement 28698
10.6%

Most occurring characters

ValueCountFrequency (%)
E 374310
13.8%
O 286236
10.6%
N 261582
9.7%
I 220644
8.2%
T 216270
8.0%
A 194388
 
7.2%
D 191946
 
7.1%
C 149076
 
5.5%
L 139470
 
5.2%
M 137382
 
5.1%
Other values (7) 532128
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2703432
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 374310
13.8%
O 286236
10.6%
N 261582
9.7%
I 220644
8.2%
T 216270
8.0%
A 194388
 
7.2%
D 191946
 
7.1%
C 149076
 
5.5%
L 139470
 
5.2%
M 137382
 
5.1%
Other values (7) 532128
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2703432
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 374310
13.8%
O 286236
10.6%
N 261582
9.7%
I 220644
8.2%
T 216270
8.0%
A 194388
 
7.2%
D 191946
 
7.1%
C 149076
 
5.5%
L 139470
 
5.2%
M 137382
 
5.1%
Other values (7) 532128
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2703432
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 374310
13.8%
O 286236
10.6%
N 261582
9.7%
I 220644
8.2%
T 216270
8.0%
A 194388
 
7.2%
D 191946
 
7.1%
C 149076
 
5.5%
L 139470
 
5.2%
M 137382
 
5.1%
Other values (7) 532128
19.7%

interest_rate
Real number (ℝ)

Distinct1302
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.006606
Minimum5.42
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-21T18:31:08.145905image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile6.17
Q18.59
median11.01
Q312.99
95-th percentile16
Maximum20
Range14.58
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation2.9787807
Coefficient of variation (CV)0.27063572
Kurtosis-0.4204075
Mean11.006606
Median Absolute Deviation (MAD)2.13
Skewness0.21377813
Sum2971783.6
Variance8.8731344
MonotonicityNot monotonic
2024-12-21T18:31:08.303829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.01 19974
 
7.4%
10.99 4824
 
1.8%
7.51 4788
 
1.8%
7.49 4122
 
1.5%
7.88 4038
 
1.5%
5.42 3648
 
1.4%
7.9 3636
 
1.3%
11.49 3084
 
1.1%
9.99 2904
 
1.1%
13.49 2850
 
1.1%
Other values (1292) 216132
80.0%
ValueCountFrequency (%)
5.42 3648
1.4%
5.43 12
 
< 0.1%
5.44 12
 
< 0.1%
5.46 6
 
< 0.1%
5.47 30
 
< 0.1%
5.48 24
 
< 0.1%
5.49 24
 
< 0.1%
5.5 6
 
< 0.1%
5.51 18
 
< 0.1%
5.52 12
 
< 0.1%
ValueCountFrequency (%)
20 504
0.2%
19.91 54
 
< 0.1%
19.9 6
 
< 0.1%
19.82 30
 
< 0.1%
19.8 6
 
< 0.1%
19.79 24
 
< 0.1%
19.74 24
 
< 0.1%
19.69 72
 
< 0.1%
19.66 18
 
< 0.1%
19.62 6
 
< 0.1%

percent_income
Real number (ℝ)

High correlation 

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13972489
Minimum0
Maximum0.66
Zeros162
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-21T18:31:08.454887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q10.07
median0.12
Q30.19
95-th percentile0.31
Maximum0.66
Range0.66
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.0872115
Coefficient of variation (CV)0.62416582
Kurtosis1.0822049
Mean0.13972489
Median Absolute Deviation (MAD)0.05
Skewness1.0344834
Sum37725.72
Variance0.0076058458
MonotonicityNot monotonic
2024-12-21T18:31:08.607088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 15558
 
5.8%
0.1 14526
 
5.4%
0.07 14490
 
5.4%
0.09 13770
 
5.1%
0.06 13452
 
5.0%
0.12 13296
 
4.9%
0.05 13056
 
4.8%
0.11 12948
 
4.8%
0.14 11760
 
4.4%
0.04 11700
 
4.3%
Other values (54) 135444
50.2%
ValueCountFrequency (%)
0 162
 
0.1%
0.01 1890
 
0.7%
0.02 5664
 
2.1%
0.03 8928
3.3%
0.04 11700
4.3%
0.05 13056
4.8%
0.06 13452
5.0%
0.07 14490
5.4%
0.08 15558
5.8%
0.09 13770
5.1%
ValueCountFrequency (%)
0.66 6
 
< 0.1%
0.63 6
 
< 0.1%
0.62 12
 
< 0.1%
0.61 12
 
< 0.1%
0.59 6
 
< 0.1%
0.58 6
 
< 0.1%
0.57 6
 
< 0.1%
0.56 30
< 0.1%
0.55 30
< 0.1%
0.54 48
< 0.1%

cred_hist_length
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8674889
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-21T18:31:08.740041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q38
95-th percentile14
Maximum30
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8796659
Coefficient of variation (CV)0.66121402
Kurtosis3.7254884
Mean5.8674889
Median Absolute Deviation (MAD)2
Skewness1.6316746
Sum1584222
Variance15.051808
MonotonicityNot monotonic
2024-12-21T18:31:08.900219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 51918
19.2%
3 49872
18.5%
2 39222
14.5%
5 18492
 
6.8%
6 17796
 
6.6%
7 17334
 
6.4%
8 16800
 
6.2%
9 16110
 
6.0%
10 14742
 
5.5%
12 4290
 
1.6%
Other values (19) 23424
8.7%
ValueCountFrequency (%)
2 39222
14.5%
3 49872
18.5%
4 51918
19.2%
5 18492
 
6.8%
6 17796
 
6.6%
7 17334
 
6.4%
8 16800
 
6.2%
9 16110
 
6.0%
10 14742
 
5.5%
11 4272
 
1.6%
ValueCountFrequency (%)
30 138
0.1%
29 90
< 0.1%
28 174
0.1%
27 138
0.1%
26 120
< 0.1%
25 138
0.1%
24 204
0.1%
23 156
0.1%
22 192
0.1%
21 144
0.1%

credit_score
Real number (ℝ)

Distinct340
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean632.60876
Minimum390
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-12-21T18:31:09.050776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum390
5-th percentile539
Q1601
median640
Q3670
95-th percentile703
Maximum850
Range460
Interquartile range (IQR)69

Descriptive statistics

Standard deviation50.435398
Coefficient of variation (CV)0.079726051
Kurtosis0.20289195
Mean632.60876
Median Absolute Deviation (MAD)33
Skewness-0.61024388
Sum1.7080436 × 108
Variance2543.7294
MonotonicityNot monotonic
2024-12-21T18:31:09.192905image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
658 2436
 
0.9%
649 2388
 
0.9%
652 2376
 
0.9%
663 2364
 
0.9%
647 2358
 
0.9%
650 2346
 
0.9%
654 2346
 
0.9%
667 2340
 
0.9%
653 2340
 
0.9%
656 2316
 
0.9%
Other values (330) 246390
91.3%
ValueCountFrequency (%)
390 6
 
< 0.1%
418 6
 
< 0.1%
419 6
 
< 0.1%
420 6
 
< 0.1%
421 6
 
< 0.1%
430 6
 
< 0.1%
431 12
< 0.1%
434 6
 
< 0.1%
435 24
< 0.1%
437 12
< 0.1%
ValueCountFrequency (%)
850 6
< 0.1%
807 6
< 0.1%
805 6
< 0.1%
792 6
< 0.1%
789 6
< 0.1%
784 12
< 0.1%
773 6
< 0.1%
772 6
< 0.1%
770 6
< 0.1%
768 6
< 0.1%

previous_defaults
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size263.8 KiB
True
137148 
False
132852 
ValueCountFrequency (%)
True 137148
50.8%
False 132852
49.2%
2024-12-21T18:31:09.308248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

loan_status
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
210000 
1
60000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters270000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 210000
77.8%
1 60000
 
22.2%

Length

2024-12-21T18:31:09.421728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T18:31:09.520256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 210000
77.8%
1 60000
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 210000
77.8%
1 60000
 
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 270000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 210000
77.8%
1 60000
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 270000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 210000
77.8%
1 60000
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 270000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 210000
77.8%
1 60000
 
22.2%

Interactions

2024-12-21T18:31:01.885041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:48.684986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:51.062280image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:52.280092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:53.558426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:54.819836image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:56.139394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:57.669535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:58.954804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:00.465545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:02.022101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:48.850636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:51.176154image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:52.403428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:53.676883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:54.941125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:56.272476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:57.795594image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:59.080955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:00.602790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:02.147635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:48.970641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:51.292719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:52.527704image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:53.798284image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:55.061079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:56.652532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:57.918943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:59.228552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:00.778153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:02.277905image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:50.165349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:51.413733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:52.660770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:53.917409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:55.180052image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:56.781216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:58.046992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:59.374449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:00.933874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:02.405874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:50.316574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:51.540438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:52.793483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:54.040822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:55.305205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:56.910626image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:58.178289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:59.512344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:01.083150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:02.530423image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:50.442277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:51.662467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:52.914161image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:54.176646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:55.428234image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:57.035100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:58.303122image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:59.640071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:01.210013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:02.685022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:50.570017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:51.796652image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:53.065750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:54.316381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:55.554862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:57.165709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:58.431172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:59.783733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:01.346577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:02.853620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:50.693874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:51.921869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:53.202170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:54.454445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:55.681805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:57.296276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:58.558707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:00.044434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:01.483722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:02.996188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:50.817134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:52.041755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:53.325783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:54.579809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:55.803410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:57.422286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:58.694751image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:00.220993image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:01.614334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:03.120399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:50.944063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:52.162377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:53.446324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:54.699986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:55.927542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:57.545994image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:30:58.823802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:00.344679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-21T18:31:01.745302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2024-12-21T18:31:09.603886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
agecred_hist_lengthcredit_scoreeducationemp_expgenderhome_ownershipincomeinterest_rateloan_amountloan_idloan_intentloan_statuspercent_incomeperson_idprevious_defaults
age1.0000.8210.1600.0610.8880.0260.0190.1430.0130.0640.0810.0330.017-0.0560.0810.032
cred_hist_length0.8211.0000.1420.0920.7500.0290.0310.0930.0170.0430.0850.0550.024-0.0370.0850.029
credit_score0.1600.1421.0000.1300.1720.0140.0110.0230.0110.0060.0110.0200.015-0.0120.0110.179
education0.0610.0920.1301.0000.0660.0030.0110.0100.0130.0130.0270.0160.0050.0110.0270.041
emp_exp0.8880.7500.1720.0661.0000.0250.0150.1200.0160.0520.0670.0320.019-0.0500.0670.031
gender0.0260.0290.0140.0030.0251.0000.0000.0130.0090.0140.0000.0060.0000.0100.0000.000
home_ownership0.0190.0310.0110.0110.0150.0001.0000.0130.0850.0910.0410.0830.2580.0920.0410.140
income0.1430.0930.0230.0100.1200.0130.0131.000-0.0330.4050.0170.0140.013-0.3530.0170.013
interest_rate0.0130.0170.0110.0130.0160.0090.085-0.0331.0000.1050.0030.0210.3630.1240.0030.198
loan_amount0.0640.0430.0060.0130.0520.0140.0910.4050.1051.0000.0110.0330.1260.6660.0110.068
loan_id0.0810.0850.0110.0270.0670.0000.0410.0170.0030.0111.0000.0210.062-0.0011.0000.025
loan_intent0.0330.0550.0200.0160.0320.0060.0830.0140.0210.0330.0211.0000.1420.0220.0210.081
loan_status0.0170.0240.0150.0050.0190.0000.2580.0130.3630.1260.0620.1421.0000.4150.0620.543
percent_income-0.056-0.037-0.0120.011-0.0500.0100.092-0.3530.1240.666-0.0010.0220.4151.000-0.0010.220
person_id0.0810.0850.0110.0270.0670.0000.0410.0170.0030.0111.0000.0210.062-0.0011.0000.025
previous_defaults0.0320.0290.1790.0410.0310.0000.1400.0130.1980.0680.0250.0810.5430.2200.0251.000

Missing values

2024-12-21T18:31:03.326185image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-21T18:31:03.947674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

loan_idperson_idagegendereducationincomeemp_exphome_ownershiploan_amountloan_intentinterest_ratepercent_incomecred_hist_lengthcredit_scoreprevious_defaultsloan_status
01122femaleMaster71948.00RENT35000.0PERSONAL16.020.493.0561No1
12221femaleHigh School12282.00OWN1000.0EDUCATION11.140.082.0504Yes0
23325femaleHigh School12438.03MORTGAGE5500.0MEDICAL12.870.443.0635No1
34423femaleBachelor79753.00RENT35000.0MEDICAL15.230.442.0675No1
45524maleMaster66135.01RENT35000.0MEDICAL14.270.534.0586No1
56621femaleHigh School12951.00OWN2500.0VENTURE7.140.192.0532No1
67726femaleBachelor93471.01RENT35000.0EDUCATION12.420.373.0701No1
78824femaleHigh School95550.05RENT35000.0MEDICAL11.110.374.0585No1
89924femaleAssociate100684.03RENT35000.0PERSONAL8.900.352.0544No1
9101021femaleHigh School12739.00OWN1600.0VENTURE14.740.133.0640No1
loan_idperson_idagegendereducationincomeemp_exphome_ownershiploan_amountloan_intentinterest_ratepercent_incomecred_hist_lengthcredit_scoreprevious_defaultsloan_status
26999026999126999131maleMaster136832.09RENT12319.0PERSONAL16.920.097.0722No1
26999126999226999224maleHigh School37786.00MORTGAGE13500.0EDUCATION13.430.364.0612No1
26999226999326999323femaleBachelor40925.00RENT9000.0PERSONAL11.010.224.0487No1
26999326999426999427femaleHigh School35512.04RENT5000.0PERSONAL15.830.145.0505No1
26999426999526999524femaleAssociate31924.02RENT12229.0MEDICAL10.700.384.0678No1
26999526999626999627maleAssociate47971.06RENT15000.0MEDICAL15.660.313.0645No1
26999626999726999737femaleAssociate65800.017RENT9000.0HOMEIMPROVEMENT14.070.1411.0621No1
26999726999826999833maleAssociate56942.07RENT2771.0DEBTCONSOLIDATION10.020.0510.0668No1
26999826999926999929maleBachelor33164.04RENT12000.0EDUCATION13.230.366.0604No1
26999927000027000024maleHigh School51609.01RENT6665.0DEBTCONSOLIDATION17.050.133.0628No1